class_docstr: Apply a random transformation to the contrast of a tensor image.
  
  This implementation aligns PIL. Hence, the output is close to TorchVision.
  
  .. image:: _static/img/RandomContrast.png
  
  Args:
      p: probability of applying the transformation.
      contrast: the contrast factor to apply.
      clip_output: if true clip output.
      same_on_batch: apply the same transformation across the batch.
      keepdim: whether to keep the output shape the same as input (True) or broadcast it
               to the batch form (False).
  Shape:
      - Input: :math:`(C, H, W)` or :math:`(B, C, H, W)`, Optional: :math:`(B, 3, 3)`
      - Output: :math:`(B, C, H, W)`
  
  .. note::
      This function internally uses :func:`kornia.enhance.adjust_contrast`
  
  Examples:
      >>> rng = torch.manual_seed(0)
      >>> inputs = torch.rand(1, 3, 3, 3)
      >>> aug = RandomContrast(contrast = (0.5, 2.), p = 1.)
      >>> aug(inputs)
      tensor([[[[0.2750, 0.4258, 0.0490],
                [0.0732, 0.1704, 0.3514],
                [0.2716, 0.4969, 0.2525]],
      <BLANKLINE>
               [[0.3505, 0.1934, 0.2227],
                [0.0124, 0.0936, 0.1629],
                [0.2874, 0.3867, 0.4434]],
      <BLANKLINE>
               [[0.0893, 0.1564, 0.3778],
                [0.5072, 0.2201, 0.4845],
                [0.2325, 0.3064, 0.5281]]]])
  
  To apply the exact augmenation again, you may take the advantage of the previous parameter state:
  
      >>> input = torch.rand(1, 3, 32, 32)
      >>> aug = RandomContrast((0.8,1.2), p=1.)
      >>> (aug(input) == aug(input, params=aug._params)).all()
      tensor(True)
code: def apply_transform(
      self,
      input: Tensor,
      params: Dict[str, Tensor],
      flags: Dict[str, Any],
      transform: Optional[Tensor] = None,
  ) -> Tensor:
      contrast_factor = params["contrast_factor"].to(input)
      return adjust_contrast(input, contrast_factor, self.clip_output)
  
file_path: kornia/kornia/augmentation/_2d/intensity/contrast.py
imports: from typing import Any, Dict, Optional, Tuple
  from kornia.augmentation import random_generator
  from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
  from kornia.augmentation.utils import _range_bound
  from kornia.core import Tensor
  from kornia.enhance.adjust import adjust_contrast
label_desc: Use image node_id 2 for calling the RandomContrast obj's underlying member method code with example usage: obj.apply_transform(input, params, flags, transform) and returns: adjust_contrast